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Enteromorpha compressa Macroalgal Biomass Nanoparticles as Eco-Friendly Biosorbents for the Efficient Removal of Harmful Metals from Aqueous Solutions

Authors :
Alaa M. Younis
Sayed M. Saleh
Abuzar E. A. E. Albadri
Eman M. Elkady
Source :
Analytica, Vol 5, Iss 3, Pp 322-342 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

This study focuses on the biosorption of harmful metals from aqueous solutions using Enteromorpha compressa macroalgal biomass nanoparticles as the biosorbent. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction analysis (XRD) were employed to characterize the biosorbent. The effects of pH, initial metal ion concentration, biosorbent dosage, and contact time on the biosorption process were investigated. The maximum biosorption capacity for metals was observed at a pH of 5.0. The experimental equilibrium data were analyzed using three-parameter isotherm models, namely Freundlich, Temkin, and Langmuir equations, which provided better fits for the equilibrium data. A contact time of approximately 120 min was required to achieve biosorption equilibrium for various initial metal concentrations. Cr(III), Co(II), Ni(II), Cu(II), and Cd(II) demonstrated distinct maximum biosorption capacities of 24.99375 mg/g, 25.06894 mg/g, 24.55796 mg/g, 24.97502 mg/g, and 25.3936 mg/g, respectively. Different kinetic models were applied to fit the kinetic data, including intraparticle diffusion, pseudo-second-order, and pseudo-first-order versions. The pseudo-second-order model showed good agreement with the experimental results, indicating its suitability for describing the kinetics of the biosorption process. Based on these findings, it can be stated that E. compressa nanoparticle demonstrates potential as an effective biosorbent for removing targeted metals from water.

Details

Language :
English
ISSN :
26734532 and 98242814
Volume :
5
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Analytica
Publication Type :
Academic Journal
Accession number :
edsdoj.285fc98242814e958379f73bea8b6592
Document Type :
article
Full Text :
https://doi.org/10.3390/analytica5030021